Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

Abstract

With a view to detecting incipient failures in large-size low-speed rolling bearings and ensuring minimal effect of subjectivity on the process, a new data-driven multivariate and multiscale statistical monitoring method is proposed. The proposed method which combines the Principal Component Analysis (PCA) multivariate monitoring approach and the Ensemble Empirical Mode Decomposition (EEMD) method, which adaptively decomposes signals into various time scales, was called the EEMD-based multiscale PCA (EEMD–MSPCA). The method is very general in nature, which is why it could also be used in different areas and for various tasks. It can be used for controlling each time scale of decomposition or only the selected ones, for multivariate and multiscale filtering or for monitoring system operation on the basis of reconstructed i.e. filtered signals. The efficiency of the proposed EEMD–MSPCA method for the task of bearing condition monitoring and signal filtering was evaluated on simulated as well as on actual vibration and Acoustic Emission (AE) signals measured on a purpose built test stand. The fact that the proposed method is able to identify the local bearing defect of a very small size indicates that AE and vibration signals carry sufficient information on the bearing condition and that the proposed EEMD–MSPCA method ensures high-reliability bearing fault detection.